Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
Claims 1-11 and 15-18 are currently pending.
Claim 12-14 and 19-20 are canceled.
Claims 1, 4, 11, 15, 16, and 18 are currently amended.
The 112(b) rejections for claims 1, 4, and 16 have been overcome.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-11 and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Hu et al. (US 20220258723 A1), and herein after will be referred to as Hu.
Regarding Claim 1, Hu teaches a control unit of a vehicle; (see at least Hu, Para [0007]: “A main controller is in communication with the torque actuators, and is programmed with calibrated constraints.”)a first torque actuator of a first power unit delivering torque to rear wheels of the vehicle, and a second torque actuator of a second power unit delivering torque to front wheels of the vehicle;(see at least Hu, Para [0006]: “a motor vehicle includes first and second drive axles respectively coupled to first and second sets of road wheels, and a plurality of torque actuators inclusive of rotary electric machines, each configured to transmit respective output torques to the first and/or second drive axles.”; see at least Hu, Para [0025]: “The eAWD propulsion system 11 includes multiple rotary electric machines (ME) 114E, including a rear propulsion motor 14 and a front propulsion motor 114”)
the priority assignments being applied to the configurations, the operating points and the configuration classifications to determine a control action as sensed vehicle operating conditions change;
(see at least Hu, Para [0008]: “The main controller also determines an optimal torque vector, as well as optimal setpoints for other considered actuators, by using a cost optimization function. The
torque vector allocates the total longitudinal torque request and/or the total longitudinal speed request, the yaw rate request, and the lateral velocity request to the first drive axle and/or the second drive axle, within/bounded by the calibrated set of constraints. A closed-loop control signal is then transmitted by the main controller to each of the torque actuators, or associated local control processors thereof, to thereby apply the torque vector via the first drive axle and/or the second drive axle.”; see at least Hu, Para [0044]: "Optimization simultaneously considers all of the costs within the cost function 51, and finds optimal actuator setpoints, e.g., a corresponding torque vector, that minimizes the cost and provides an optimal tradeoff between objectives."; see at least Hu, Para [0043]: "To implement the cost optimization function 51 used herein, for instance, the main controller 50 may be programmed with relevant tracking functions, e.g., for desired longitudinal velocity, longitudinal torque request, desired yaw rate, etc., while constraining for the above noted set of constraints."; The "costs" within the function are the priority assignments of weights and penalties. The function considers the motion request ("configurations") that is bounded by constraints and is influenced by the operating points (the weights) for a control action. The process responds to changing conditions where the controller receive "tracking functions" for the desired longitudinal velocity, longitudinal torque request, desired yaw, etc. while constraining for the above constraints.)
Hu does not explicitly teach multiple sensors to sense multiple input items received by the control unit, the multiple input items including: multiple configurations; one or more operating points; multiple configuration classifications including: a torque constraint; a torque reference; and an enabling condition; and identification of one or more priority assignments; and one of the configurations being mapped to a normalized torque split ratio.
Regarding multiple sensors to sense multiple input items received by the control unit, Hu teaches receiving a set of input signals including a steering angle, an accelerator request signal, and a braking request signal (see at least Hu, Para [0030]: “a steering wheel 22S, impact a steering angle (arrow Bx), which is read by the main controller 50 as part of a set of input signals (arrow CCI), along with the accelerator request signal (arrow Ax) and braking request signal (arrow Bx)…”). It would have been obvious to one of ordinary skill in the art that generating control signals such as steering angle, accelerator request, and braking request necessarily requires the corresponding vehicle sensors (e.g., steering angle sensor, throttle position sensor, braking pressure sensor). The use of multiple distinct sensors to obtain such input signals is well-understood, routine, and conventional in the field of vehicle control systems.Regarding the multiple input items including: multiple configurations; one or more operating points; multiple configuration classifications including: a torque constraint; a torque reference; and an enabling condition; and identification of one or more priority assignments, Hu teaches that the set of sensor signals is received by the main controller and used to compute control objectives such as lateral and longitudinal request, yaw rate, and lateral velocity (see at least Hu Para [0040]: “the main controller 50 calculates, using the set of vehicle inputs from block B102, separate total lateral and longitudinal torque or motion requests… the main controller 50 may calculate a yaw rate request and a lateral velocity request of the motor vehicle 10…”).
Hu teaches calculating motion request such as “yaw rate request, and a lateral velocity request” (see at least Hu, Para [0040]: “the main controller 50 calculates…motion requests…the main controller 50 may calculate a yaw rate request and a lateral velocity request…”) where the calculated request are specific data sets that define and configure the controller’s objectives for multiple configurations.
Hu teaches that the controller modifies the weightings of the cost optimization function based on a selectable “mode selection signal” where the selectable mode defines specific operating points of the vehicle that changes the control behavior (see at least Hu, Para [0046]: “…the main controller 50 could receive the mode selection signal…The main controller 50 could then modify weighting within the above-noted cost optimization functions in response to the mode selection signal.”).
Furthermore, Hu teaches exemplary constraints is applied by the main controller for the most efficient torque split between the rear and front axles and constraining each assigned axle torque equivalent to a torque constraint (see at least Hu, Para [0045]: “Exemplary constraints that could be taken into consideration by the main controller 50…the tracking of a most efficient torque split between the drive axles 119F and 119R…constraining each assigned axle torque”). The controller receives a “total torque request (TREQ)” from the driver by the acceleration pedal that is used in the cost optimization function which corresponds to a torque reference (see at least Hu, Para [0039] “...driver of the motor vehicle 10 in FIG. 1 may generate the total torque request (TREQ)..”). Hu also teaches the main controller receiving a mode selection signal from a mode selection device that can be operator-requested or autonomously-requested (see at least Hu, Para [0046]: “…the main controller 50 could receive the mode selection signal… whether operator-requested or autonomously-requested. The main controller 50 could then modify weighting within the above-noted cost optimization functions in response to the mode selection signal.”). This mode selection signal corresponds to the enabling condition because it is a specific trigger for a change in control behavior.
Additionally, Hu describes a method of using relative weighting to select a priority (see at least Hu, Para [0061]: "Relative weighting of the associate costs or penalties are used to select a priority between different control objectives…”) that determines a priority between the different control objectives that corresponds to the identification of one or more priority assignments.
Regarding one of the configurations being mapped to a normalized torque split ratio, Hu teaches determining a torque vector for allocating torque between the front and rear drive axles of the vehicle (see at least Hu, Para [0042]: “…the main controller 50 using the cost optimization function (fOPT) 51 of FIG. 1, a torque vector {right arrow over (T)} for allocating the total longitudinal torque request and/or the total longitudinal speed request, the yaw rate request, and the lateral velocity request to the front drive axle 119F and/or the rear drive axle…”) where the longitudinal torque request (configurations) is split between the axles. Hu provides an example of a simplified three-motor/dual axle system and torque vector that is represented as T = [A,B,C], where each letter in the vector corresponds to a specific torque value to be sent to a different drive unit. The controller calculates these values and the resulting vector defines how the torque is split among the vehicle’s actuators, which is functionally a torque split ratio. In control systems, defining the relationship between inputs and outputs to determine the system’s behavior is the process of mapping. Normalization of sensor values is a well-known and standard practice that ensures stable and predictable system behavior in control systems management.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the control logic to provide the multiple sensors to sense multiple input items received by the control unit, the multiple input items including: multiple configurations; one or more operating points; multiple configuration classifications including: a torque constraint; a torque reference; and an enabling condition; and identification of one or more priority assignments; and one of the configurations being mapped to a normalized torque split ratio. It would have been obvious to one of ordinary skill in the art for vehicle control systems that the multiple sensors to sense multiple input items include torque related and operating parameters to optimize the torque split ratio between the axles of the vehicle. This provides the benefit of improving vehicle performance under varying operating conditions.
Regarding Claim 2, Hu teaches the system of claim 1 as discussed above. Hu further teaches the configurations being categorized as reference points or constraints (see at least Hu, Para [0007]: “A main controller is in communication with the torque actuators, and is programmed with calibrated constraints. The main controller is configured to receive a set of vehicle inputs indicative of a total longitudinal motion request and a total lateral motion request of the motor vehicle, and to calculate, using the vehicle inputs, a total longitudinal torque request and/or a total longitudinal speed request, a yaw rate request, and a lateral velocity request of the motor vehicle.”).
Regarding Claim 3, Hu teaches all the limitations of claim 2 as discussed above. Hu further teaches the constraints further include a set of operating constraints including an understeering angle, a lateral acceleration, a velocity, a yaw rate, a desired drive torque and an estimated drive torque (see at least Hu, Para [0008]: “The torque vector allocates the total longitudinal torque request and/or the total longitudinal speed request, the yaw rate request, and the lateral velocity request to the first drive axle and/or the second drive axle, within/bounded by the calibrated set of constraints.”).
Regarding Claim 4, Hu teaches all the limitations of claim 3 as discussed above. Hu further teaches a system for lateral motion control that uses indicators of the vehicle’s lateral state such as “yaw rate request” and “lateral velocity request” to determine an optimal torque allocation between the axles of the vehicle (see at least Hu, Para [0036]: “The method 100 of FIG.2 is intended to incorporate lateral vehicle dynamics objectives into a torque control architecture…lateral motion objectives such as desired yaw rate and lateral velocity are used as optimization objectives. This occurs in addition to the traditional longitudinal objectives typically determined using a driver's total torque and speed requests.”).
Hu does not explicitly teach a normalized measure of lateral stability is identified from the constraints and indicators of a vehicle lateral state, the indicators including a configuration of steering neutrality and lateral stability by lateral motion control; and an axle torque split ratio upper limit and an axle torque split ratio lower limit are also determined from the normalized measure of lateral stability, wherein lateral motion control is enabled when at least one wheel speed sensor is functional, at least one inertial measurement unit (IMU) is functional, and a steering sensor is functional.
wherein lateral motion control is assigned a highest priority across all operating points.
Regarding a normalized measure of lateral stability is identified from the constraints and indicators of a vehicle lateral state, the indicators including a configuration of steering neutrality and lateral stability by lateral motion control, Hu teaches an optimization process that considers the lateral objectives of the vehicle (see at least Hu, Para [0036]: “…lateral motion objectives such as desired yaw rate and lateral velocity are used as optimization objectives”) and calculates a safe torque vector within a set of constraints (see at least Hu, Para [0042]: "…the main controller 50 using the cost optimization function…within the calibrated set of constraints noted above.") and indicators (see at least Hu, Para [0043]: "...the dynamic model used for optimization provides the dynamic relationship between the manipulated actuators, e.g., torque distribution, friction brake torques, rear steering, etc., and vehicle dynamic states such as longitudinal velocity/acceleration, lateral velocity/acceleration, yaw rate, wheels speeds, etc."). It would have been obvious to one of ordinary skill in the art that lateral stability of a vehicle in motion would require the controller to have defined constraints and indicators that include steering neutrality from the yaw rate and lateral stability by lateral motion control from the vehicle dynamic state lateral velocity/acceleration that are taught in Hu, for safety operations of the vehicle. Normalization is an inherent and expected characteristics of signals in control systems.
Regarding an axle torque split ratio upper limit and an axle torque split ratio lower limit are also determined from the normalized measure of lateral stability, Hu teaches that the controller uses optimization constraints to limit the outcome of the torque vectoring control including the maximum torque values across axles (see at least Hu, Para [0057]: “The optimization constraints 51C likewise limit the optimization outcomes, such as by enforcing calibrated maximum toque to the sum of the individual axle torques…”). Regarding lateral motion control is enabled when at least one wheel speed sensor is functional, at least one inertial measurement unit (IMU) is functional, and a steering sensor is functional, Hu teaches the optimization model utilizes vehicle states such as yaw rate, wheels speeds, etc. (see at least Hu, Para [0043]: “"...the dynamic model used for optimization provides the dynamic relationship between the manipulated actuators, e.g., torque distribution, friction brake torques, rear steering, etc., and vehicle dynamic states such as longitudinal velocity/acceleration, lateral velocity/acceleration, yaw rate, wheels speeds, etc."”) to calculate the lateral objectives of the vehicle, which inherently requires input from wheel speed sensors and a functional inertial measurement unit (IMU). Additionally, Hu teaches reading the steering angle (see at least Hu, Para [0030]: “An operator of the motor vehicle 10 may, using a steering wheel 22S, impact a steering angle (arrow Bx),”) that requires the use of a functional steering angle sensor.
Regarding lateral motion control is assigned a highest priority across all operating points, Hu teaches implementing control objectives as fixed hard constraints that cannot be violated (see at least Hu, Para [0044]: "Constraints can be both soft and hard depending on whether or not the constraint can be occasionally violated (soft) or not (hard)."). Hu further discloses that external requestors can be assigned the highest priority or weight in the arbitration process such as for stability control (see at least Hu, Para [0058]: "Thus, external requestors have override priority in determining axle torque requests are arbitrated after optimization of the axle torque requests. A possible implementation in the optimization scheme therefore includes imposing the external requestor with a highest priority or weight as an additional hard constraint on the affected axle(s)."). It would have been obvious to one of ordinary skill in the art to assign lateral motion control the highest priority across all operating points to maintain vehicle stability during all driving conditions.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the control system of Hu to incorporate a normalized measure of lateral stability is identified from the constraints and indicators of a vehicle lateral state, the indicators including a configuration of steering neutrality and lateral stability by lateral motion control; and an axle torque split ratio upper limit and an axle torque split ratio lower limit are also determined from the normalized measure of lateral stability, wherein lateral motion control is enabled when at least one wheel speed sensor is functional, at least one inertial measurement unit (IMU) is functional, and a steering sensor is functional, wherein lateral motion control is assigned a highest priority across all operating points. This provides the benefit of ensuring that reliability and safety are prioritized in the torque control system during all driving scenarios.
Regarding Claim 5, Hu teaches all the limitations of claim 2 as discussed above. Hu further teaches a priority-based arbitration as a function of the vehicle operating mode (see at least Hu, Para [0037]:” …multi-objective optimization/arbitration to determine an optimum torque distribution over multiple axles.”), the control unit accessing individual ones of the multiple configurations to establish a vehicle operating mode (see at least Hu, Para [0015]: “…mode selection device may be configured to receive an operator-requested or autonomously requested mode selection signal, with the main controller configured to modify weights within the cost optimization function in response to the mode selection signal.”).
Regarding Claim 6, Hu teaches the system of claim 1 as discussed above. Hu further teaches the configurations are prioritized, including a first configuration deemed to be a highest priority and indicated as priority 1 and a second configuration deemed of a next highest priority and indicated as priority 2 (see at least Hu, Para [0008]: “The main controller also determines an optimal torque vector, as well as optimal setpoints for other considered actuators, by using a cost optimization function.”).
Regarding Claim 7, Hu teaches the system of claim 1 as discussed above. Hu further teaches the multiple configurations include at least one of: a desired torque; a traction value; propulsion limits; a slip target; a driver request; an eBoost signal from a second power unit; a steering neutrality; a total torque reduction; a traction control system (TCS) integration; and a rear traction limits overflow (see at least Hu, Para [0046]: “…the main controller 50 could receive the mode selection signal (arrow MxofFIG. 1) from the mode selection device 22M, whether operator-requested or autonomously-requested.; see at least Hu, Para [0051]: “After performing such optimization at block B106 of FIG. 2, the method 100 proceeds to block B108, with the main controller 50 determining external limits or axle interventions. Such limits could be communicated to the main controller 50 from a different control unit, e.g., an electronic stability control or traction control module, or such limits could originate from different functions residing aboard the main controller.”).
Regarding Claim 8, Hu teaches the system of claim 1 as discussed above. Hu further teaches the operating points include at least one of: a drive mode including tour or track; a traction control state including performance traction management (PTM) or electronic stability control (ESC); a surface friction (Mu); and a highway driving point (see at least Hu, Para [0041]: “…the system estimating vehicle conditions such as velocity, attitude, and propulsor states that are essential for selecting driving modes including tour or track. The system evaluating wheel slip that is fundamental to PTM or ESC operations and surface friction operating point.”).
Regarding Claim 9, Hu teaches the system of claim 1 as discussed above. Hu further teaches the first power unit defines a combustion engine (see at least Hu, Para [0001]: “Multiple electric propulsion motors could be used in some electrified powertrain configurations, either alone or in conjunction with an internal combustion engine.”); and the second power unit defines an electric motor (see at least Hu, Para [0026]: “Other powertrain components may be included within the eAWD propulsion system 11, such as but not limited to an optional internal combustion engine (E) 200 with an output shaft 201 providing an engine torque (arrow TE) in a possible hybrid electric configuration”).
Regarding Claim 10, Hu teaches the system of claim 1 as discussed above. Hu further teaches the multiple input items include identification of one or more operating points, including: a drive mode including tour or track; a traction control state including performance traction management (PTM) or electronic stability control (ESC); a surface friction (Mu) (see at least Hu, Para [0041]: “the system estimating vehicle conditions such as velocity, attitude, and propulsor states that are essential for selecting driving modes including tour or track. The system evaluating wheel slip that is fundamental to PTM or ESC operations and surface friction operating point.”); and a highway driving point (see at least Hu, Para [0043]: “highway driving point being managed by the main controller tracking longitudinal velocity necessary for cruise control or optimization functions.”).
Regarding Claim 11, Hu teaches a method to map control system configurations for vehicle torque actuators, comprising:
translating torque elements to allow differing torque items to be evaluated and modified using a common database (see at least Hu, Para [0025]: “Instructions for implementing a torque distribution control strategy…Such instructions may be recorded in memory (M) of the controller 50 and executed…with memory (M) programmed with a cost optimization function…”; The cost optimization function is the instructions that evaluates and provides instructions to modify torque elements using the system’s memory as a common database)
performing a configurations prioritization from the(see at least Hu, Para [0061]: “Relative weighting of the associate costs or penalties are used to select a priority between different control objectives, with such costs possibly tuned using calibratable or selectable weights based on driving conditions or operating mode… coordinating operation of different torque actuators arranged on different drive axles to achieve both longitudinal and lateral vehicle control objectives.”; The system uses relative weighting on different control objectives (group of configurations) to select a priority that determines a final torque distribution on the vehicle’s torque actuators (torque elements));
fusing the configurations prioritization according to the torque elements and the priority assigned together; (see at least Hu, Para [0037]: “In particular, execution of the method 100 involves multi-objective optimization/arbitration to determine an optimum torque distribution over multiple axles, such as the representative drive axles 119F and 119R of FIG. 1 or their half-axle variants. Axle-based arbitration is then used after optimization to provide additional flexibility to enforce external axle-based interventions or other performance limits as needed to protect underlying hardware, operating limits, stability or other dynamic limits, etc.”; multi-objective optimization/arbitration is the process of prioritizing and fusing competing objectives to determine the final torque distribution over the vehicles axles);
determining multiple control actions; (see at least Hu, Para [0008]: “The main controller also determines an optimal torque vector, as well as optimal setpoints for other considered actuators, by using a cost optimization function.”; The controller determines control actions for torque vector and setpoints)
translating at least a first one of the multiple control actions as a lateral stability motion control into a torque bias ratio and assigning a maximum of a minimum constraint (see at least Hu, Para [0042]: “…the main controller 50 using the cost optimization function (foPT) 51 of FIG. 1, a torque vector T for allocating the total longitudinal torque request and/or the total longitudinal speed request, the yaw rate request, and the lateral velocity request to the front drive axle 119F and/or the rear drive axle 119R within the calibrated set of constraints noted above.”; see at least Hu, Para [0057]: “The optimization constraints 51C likewise limit the optimization outcomes, such as by enforcing calibrated maximum toque to the sum of the individual axle torques, or restricting vehicle speed to a speed constraint, or ensuring axle torque requests satisfy propulsion system constraints such as battery power limits, a wheel slip ratio, etc.”; The method determines a final torque vector (torque bias ratio) to meet lateral stability objectives like yaw rate while being bounded by optimization constraints like maximum torque or restricting vehicle speed to a speed constraint for lateral stability motion control);
translating at least a second one of the multiple control actions as a wheel stability control into the torque bias ratio and assigning a reference point (see at least Hu, Para [0047]: “The torque vector could likewise be optimized at block B106 for wheel slip of the front and/or rear road wheels 15F and/or 15R in a similar manner, such as by penalizing distributions that would result in wheel slip, or that would exacerbate existing wheel slip conditions at one or more of the road wheels 15F and/or 15R.”; see at least Hu, Para [0045]: “Exemplary constraints that could be taken into consideration by the main controller 50 may include, but are not limited to, the tracking of a most efficient torque split between the drive axles 119F and 119R and/or the various road wheels 15F and 15R, constraining wheel slip to a given slip ratio, constraining each assigned axle torque”; The method optimizes the torque vector (torque bias ratio) to control wheel slip by regulating it to a “slip ratio”, which serves as the reference point for the stability action);
and one of the operating points including one of: an axle torque split ratio reference point of an axle torque split ratio (see at least Hu, Para [0007]: “The main controller is configured to receive a set of vehicle inputs indicative of a total longitudinal motion request and a total lateral motion request of the motor vehicle, and to calculate, using the vehicle inputs, a total longitudinal torque request and/or a total longitudinal speed request, a yaw rate request, and a lateral velocity request of the motor vehicle.”; The total longitudinal request is the reference point that the system uses to calculate the torque split ratio);
one or more axle torque split ratio constraints of the axle torque split ratio (see at least Hu, Para [0045]: "Exemplary constraints that could be taken into consideration by the main controller 50 may include, but are not limited to, the tracking of a most efficient torque split between the drive axles 119F and 119R and/or the various road wheels 15F and 15R, constraining wheel slip to a given slip ratio, constraining each assigned axle torque to a corresponding estimated tire capacity, constraining longitudinal velocity for overspeed control, or constraining the total torque to enforce external total torque constraints."; Multiple constraints are applied to the torque split ratio such as tire capacity or other external factors);
and control system configurations including a fuel economy (see at least Hu, Para [0049]: " The main controller 50 could also optimize the T torque vector for propulsion efficiency of the motor vehicle 10, i.e., by returning solutions that favor energy efficiency over other factors such as speed or cornering performance"; The propulsion efficiency (analogous to the “fuel economy”) is a configuration or objective the system can optimize the vehicle’s operations);
Hu does not explicitly teach sensing with a sensor a group of configurations to be received by a control unit, generating a target delta slip speed as measures of vehicle axle saturation; and regulating the target delta slip speed using a nonlinear proportional integral derivative (PID) regulator, translating torque elements includes: a mapping stage given sets of the group of configurations (Ri) and a set of operating points (o1) multiple pairs (Ri, o1) mapping to individual configurations, mapping to one of the multiple pairs from (Ri, o1) to (rm, on) the following items, including: 1) enabling conditions; and 2) the priority.
Regarding sensing with a sensor a group of configurations to be received by a control unit, Hu teaches using state estimation to monitor vehicle parameters such a velocity, pitch, yaw, roll, tire pressure, and wheel slip where this group of parameters is used an input to the cost optimization function by the main controller (see at least Hu, Para [0041]: " As appreciated in the art, state estimation is typically used in vehicular applications to monitor, e.g., present velocity, attitude (pitch, yaw, and roll)…The present state of the motor vehicle 10 is therefore fed into the cost optimization function 51 of FIG. 1, such that the main controller 50 is aware of the present state before commencing optimization calculations specific to the method 100. "). A person of ordinary skill in the art would recognize that an inertial measurement unit (IMU), which can measure pitch, yaw, and roll, is a single sensor capable of sensing a group of configurations relevant to vehicle motions. It would have been obvious to use an IMU sensor to provide multiple configuration inputs to the control unit to enhance control objectives.
Regarding generating a target delta slip speed as measures of vehicle axle saturation and regulating the target delta slip speed using a nonlinear proportional integral derivative (PID) regulator, Hu teaches optimizing the torque vector based on wheel slip behavior (see at least Hu, Para [0047]: "The torque vector could likewise be optimized at block B106 for wheel slip…”) and that the torque distribution to different axles could be optimized for wheel slip (see at least Hu, Para [0048]: “…torque distribution to different axles could be optimized for wheel slip…”). It would have been obvious to one of ordinary skill in the art of vehicle control systems that optimizing for wheel slip requires determining the delta slip speed between the wheels or axles, as the delta slip speed serves as the basis for identifying and correcting traction balances and reflects the degree of axle saturation which is critical for correcting traction loss. Further, it would have been obvious to one of ordinary skill in the art to use a nonlinear PID controller to regulate slip which allows the control system to respond more accurately and stabilize control by adapting the gain response across different slip magnitudes.
Regarding translating torque elements includes: a mapping stage given sets of the group of configurations (Ri) and a set of operating points (o1) multiple pairs (Ri, o1) mapping to individual configurations, Hu teaches tracking the most efficient torque split (see at least Hu, Para [0045]: “…the tracking of a most efficient torque split between the drive axles”), a mode selection signal that modifies the weighting of the cost optimization function (see at least Hu, Para [0046]: “The main controller 50 could then modify weighting within the above-noted cost optimization functions in response to the mode selection signal.”), and the dynamic tradeoff between efficiency and lateral response (see at least Hu, Para [0050]: “…the optimization function 51 and attendant control strategy would make a tradeoff between efficiency and lateral request based on how heavily each is weighted.”) implies an internal mapping framework. In the art of control systems, mapping refers to the process of defining relationships between system inputs, outputs, or configurations to guide control decisions. This framework associates specific control configurations (Ri) are associated with corresponding operating points (o1) (such as sports mode or efficient mode) to influence how torque is prioritized and executed.
Regard mapping to one of the multiple pairs from (Ri, o1) to (rm, on) the following items, including: 1) enabling conditions; and 2) the priority, Hu teaches that the main controller modifies the weighting of the cost optimization function in response to a mode selection signal (see at least Hu, Para [0046]: "the main controller 50 could receive the mode selection signal."). This modification changes the lateral performance objectives being prioritized over other factors (like efficiency or performance).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the control system of Hu to incorporate sensing with a sensor a group of configurations to be received by a control unit, generating a target delta slip speed as measures of vehicle axle saturation; and regulating the target delta slip speed using a nonlinear proportional integral derivative (PID) regulator, translating torque elements includes: a mapping stage given sets of the group of configurations (Ri) and a set of operating points (o1) multiple pairs (Ri, o1) mapping to individual configurations, mapping to one of the multiple pairs from (Ri, o1) to (rm, on) the following items, including: 1) enabling conditions; and 2) the priority. A person of ordinary skill in the art would have been motivated to use conventional control strategies such as nonlinear PID regulation of slip speed as well as a structured mapping framework for selecting control configurations. This provides the benefit of a stable and efficient control strategy for optimizing torque distribution based on slip conditions and external inputs.
Regarding Claim 16, Hu teaches the method of claim 11 as discussed above. Hu further teaches a system configured for lateral motion control where the lateral motion objectives such a desire yaw rate and lateral velocity as optimization objectives to determine a final torque allocation (Para [0036]). The system then uses the objectives to determine a control action where the torque vector allocates the request to the axles of the vehicle bounded by the calibrated set of constraints (Para [0008]).
Hu does not explicitly teach translating indicators of a vehicle lateral state to a normalized measure of lateral stability, which admits the constraints on the axle torque split ratio, the indicators including a steering neutrality and lateral stability configuration by lateral motion control.
Regarding translating indicators of a vehicle lateral state to a normalized measure of lateral stability, which admits the constraints on the axle torque split ratio, Hu teaches allocating yaw rate and lateral velocity request, both of which are indicators of the vehicle’s lateral state, to individual axles using a torque vector that is bounded by a calibrated set of constraints (see at least Hu, Para [0008]: “The torque vector allocates the total longitudinal torque request and/or the total longitudinal speed request, the yaw rate request, and the lateral velocity request to the first drive axle and/or the second drive axle, within/bounded by the calibrated set of constraints.”). This reflects a process in which lateral state indicators are translated into control objectives that are evaluated within constraint boundaries.
Regarding the indicators including a steering neutrality and lateral stability configuration by lateral motion control, Hu teaches incorporating lateral vehicle objectives, such as the desired yaw rate and lateral velocity, into a torque control strategy that is executed by the main controller (see at least Hu, Para [0036]: “The method 100 of FIG. 2 is intended to incorporate lateral vehicle dynamics objectives into a torque control architecture, executed proactively by the main controller 50. As part of the present strategy, lateral motion objectives such as desired yaw rate and lateral velocity are used as optimization objectives.”). Furthermore, Hu discloses that lateral performance objectives may be prioritized in response to a selectable mode, such as “sports mode”, using weighted cost functions (see at least Hu, Para [0046]: “The main controller 50 could then modify weighting within the above-noted cost optimization functions in response to the mode selection signal…if a driver selects "sport mode", lateral performance objectives, such as meeting a driver-desired yaw rate…”). The lateral objectives inherently reflect a lateral stability control configuration.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the control system of Hu to incorporate translating indicators of a vehicle lateral state to a normalized measure of lateral stability, which admits the constraints on the axle torque split ratio, the indicators including a steering neutrality and lateral stability configuration by lateral motion control. This provides the benefit of improving vehicle stability and preventing loss of control during vehicle operations.
Regarding Claim 17, Hu teaches the method of claim 16 as discussed above. Hu further teaches translating a control action for wheel stability control into a torque bias ratio and assigning as a reference point (see at least Hu, Para [0012]: “the torque vector is configured to optimize wheel slip of the first and/or the second sets of road wheels.”; ).
Regarding Claim 18, Hu teaches delivering torque to rear wheels of a vehicle using a first torque actuator of a first power unit, and delivering torque to front wheels of the vehicle using a second torque actuator of a second power unit; (see at least Hu, Para [0006]: “a motor vehicle includes first and second drive axles respectively coupled to first and second sets of road wheels, and a plurality of torque actuators inclusive of rotary electric machines, each configured to transmit respective output torques to the first and/or second drive axles.”; see at least Hu, Para [0025]: “The eAWD propulsion system 11 includes multiple rotary electric machines (ME) 114E, including a rear propulsion motor 14 and a front propulsion motor 114”);
translating elements of the torque into a domain to allow differing ones of the elements of the torque to be evaluated and modified using a common database (see at least Hu, Para [0025]: “Instructions for implementing a torque distribution control strategy…Such instructions may be recorded in memory (M) of the controller 50 and executed…with memory (M) programmed with a cost optimization function…”; The cost optimization function is the instructions that evaluates and provides instructions to modify torque elements using the system’s memory as a common database and determines the final torque distribution);
coordinating a…lateral motion control (see at least Hu, Para [0036]: “The method 100 of FIG.2 is intended to incorporate lateral vehicle dynamics objectives into a torque control architecture, executed proactively by the main controller 50. As part of the present strategy, lateral motion objectives such as desired yaw rate and lateral velocity are used as optimization objectives.” The controller executes lateral vehicle dynamic objects for lateral motion objectives);
coordinating…an external traction control system (see at least Hu, Para [0053]: “Block Bll0 includes performing axle-based arbitration (ARB T AXL) via the main controller 50. As a possible implementation of block Bll0, such arbitration could include determining, via the main controller 50, whether to follow an optimal torque request generated at block B106, or the request from the external function and limits applied in blocks B108 and B109. Weighting of an external requester function ensures that the main controller 50 selects the request from the external function under appropriate conditions, e.g., during a high-slip traction control event.”; The controller prioritizes then coordinates an external request through an axle-based arbitration)
performing a configurations prioritization (see at least Hu, Para [0061]: “Relative weighting of the
associate costs or penalties are used to select a priority between different control objectives, with such costs possibly tuned using calibratable or selectable weights based on driving conditions or operating mode.”; The weightings prioritize the control objectives of the controller applied to the vehicle to perform a configuration prioritization).
mapping individual ones of the group of configurations to a…torque split ratio (see at least Hu, Para [0042]: “…the method 100 includes determining, via the main controller 50 using the cost optimization function (foPT) 51 of FIG. 1, a torque vector T for allocating the total longitudinal torque request and/or the total longitudinal speed request, the yaw rate request, and the lateral velocity request to the front drive axle 119F and/or the rear drive axle 119R within the calibrated set of constraints noted above.”; The method determines a torque vector that allocates the torque between the front and rear axle as a torque split ratio)
Hu does not explicitly teach sensing with a sensor a group of configurations to be received by a control unit, coordinating a feedback-based delta slip-speed regulation control and a lateral motion control; coordinating a hybrid boost control; performing non-linear gain scheduling of a controller to regulate dynamics of a wheel slip; and performing a configurations prioritization using the group of configurations, one or more operating points, multiple configuration classifications including: a torque constraint, a torque reference, and an enabling condition to identify a priority to be assigned to individual ones of the elements of the torque; and mapping individual ones of the group of configurations to a normalized torque split ratio
Regarding sensing with a sensor a group of configurations to be received by a control unit, Hu teaches using state estimation to monitor vehicle parameters such as velocity, pitch, yaw, roll, tire pressure, propulsor states, and wheel sleep, which are provided to the main controller to support cost optimization calculations (see at least Hu, Para [0041]: " As appreciated in the art, state estimation is typically used in vehicular applications to monitor, e.g., present velocity, attitude (pitch, yaw, and roll)…The present state of the motor vehicle 10 is therefore fed into the cost optimization function 51 of FIG. 1, such that the main controller 50 is aware of the present state before commencing optimization calculations specific to the method 100. "). It would have been obvious to one of ordinary skill in the art that these parameters represent a group of configurations relevant to vehicle controls and it is well understood that a single sensor, such as an inertial measurement unit (IMU) is capable of sensing multiple parameters like pitch, yaw, and roll.
Regarding coordinating a feedback-based delta slip-speed regulation control and a lateral motion control, Hu teaches that the main controller receives state information including wheel slip and uses this information to allocate torque to the front and rear axles for lateral motion control (see at least Hu, Para [0008]: “The torque vector allocates the total longitudinal torque request and/or the total longitudinal speed request, the yaw rate request, and the lateral velocity request to the first drive axle and/or the second drive axle…”; see at least Hu, Para [0041]: “State estimation may also consider…current or impending wheel slip of one or more of the road wheels 15R an 15F, etc.…The present state of the motor vehicle 10 is therefore fed into the cost optimization function 51…”). Hu further discloses that external requestors, such as a traction control system (TCS), can influence torque arbitration (see at least Hu, Para [0053]: “… performing axle-based arbitration…arbitration could include determining, via the main controller 50, whether to follow an optimal torque request generated at block B106, or the request from the external function and limits... Weighting of an external requester function ensures that the main controller 50 selects the request from the external function under appropriate conditions, e.g., during a high-slip traction control event.”). It would have been obvious to one of ordinary skill in the art of vehicle control systems that coordinating torque control with slip related signals from state estimation and TCS inherently involves feedback-based delta slip speed regulation control to ensure traction management during vehicle operations.
Regarding coordinating a hybrid boost control, Hu teaches an eAWD propulsion system with both a front and rear torque actuator driven by separate power units (see at least Hu, Para [0025]: “The eAWD
propulsion system 11 includes multiple rotary electric machines (ME) 114E, including a rear propulsion motor 14 and a front propulsion motor 114”) that includes other powertrain components such as an internal combustion engine (see at least Hu, Para [0026]: “Other powertrain components may be included within the eAWD propulsion system 11, such as but not limited to an optional internal combustion engine (E) 200…”). Hu also teaches a selectable “sports mode” that modifies optimization priorities to favor performance objectives over efficiency (see at least Hu, Para [0046]: “For instance, if a driver selects "sport mode", lateral performance objectives, such as meeting a driver-desired yaw rate, may be prioritized over factors such as powertrain efficiency…”). It would have been obvious to one of ordinary skill in the art that enabling “sports mode” prioritizes performance objectives requires coordinated torque delivery from the engine and electric motor for a hybrid boost control.
Regarding performing non-linear gain scheduling of a controller to regulate dynamics of a wheel slip, Hu teaches that the main controller monitors wheel slip and adjusts torque distribution to mitigate excessive slip conditions by penalizing torque distributions that induce wheel slippage (see at least Hu, Para [0047]: “The torque vector could likewise be optimized…for wheel slip of the front and/or rear road wheels 15F and/or 15R …by penalizing distributions that would result in wheel slip, or that would exacerbate existing wheel slip conditions at one or more of the road wheels 15F and/or 15R.”). It would have been obvious to one of ordinary skill in the art that the controller’s response changing based on slip conditions to maintain traction stability is a form of non-linear gain scheduling.
Regarding performing a configurations prioritization using the group of configurations, one or more operating points, multiple configuration classifications including: a torque constraint, a torque reference, and an enabling condition to identify a priority to be assigned to individual ones of the elements of the torque, Hu teaches that the main controller uses a cost optimization function to determine a torque vector that allocates control objectives, such as total longitudinal torque, yaw rate, and lateral velocity request, between the front and rear axles (see at least Hu, Para [0042]: “…the method 100 includes determining, via the main controller 50 using the cost optimization function (foPT) 51 of FIG. 1, a torque vector T for allocating the total longitudinal torque request and/or the total longitudinal speed request, the yaw rate request, and the lateral velocity request to the front drive axle 119F and/or the rear drive axle 119R within the calibrated set of constraints noted above.”). Theses torque related objectives represent a group of configurations that is used by cost optimization function to determine an objective. The controller’s objective varies based on the selected able drive modes like sports mode, which defines operating points that influence how objectives are weighted and prioritized (see at least Hu, Para [0046]: “The main controller 50 could then modify weighting within the above-noted cost optimization functions in response to the mode selection signal…if a driver selects "sport mode", lateral performance objectives, such as meeting a driver-desired yaw rate…”).
Hu further discloses configuration classifications including: torque constraints, that limit torque based on slip ratio, tire capacity, longitudinal velocity, or external total torque constraints (see at least Hu, Para [0045]: “Exemplary constraints that could be taken into consideration by the main controller 50 may include, but are not limited to, the tracking of a most efficient torque split between the drive axles 119F and 119R and/or the various road wheels 15F and 15R, constraining wheel slip to a given slip ratio, constraining each assigned axle torque to a corresponding estimated tire capacity, constraining longitudinal velocity for overspeed control, or constraining the total torque to enforce external total torque constraints.”); Torque reference, where a driver-generated total torque request is inputted into the controller of the vehicle (see at least Hu, Para [0039]: “...driver of the motor vehicle 10 in FIG. 1 may generate the total torque request (TREQ)..”; see at least Hu, FIG. 1); and enabling conditions, such as an external request from the mode selection device that enable specific objectives and conditions of the cost optimization function (see at least Hu, Para [0053]: “…the main controller 50 could receive the mode selection signal… whether operator-requested or autonomously-requested. The main controller 50 could then modify weighting within the above-noted cost optimization functions in response to the mode selection signal.”).
Regarding mapping individual ones of the group of configurations to a normalized torque split ratio, Hu teaches determining a torque vector for allocating torque between the front and rear drive axles of the vehicle (see at least Hu, Para [0042]: “…the main controller 50 using the cost optimization function (fOPT) 51 of FIG. 1, a torque vector {right arrow over (T)} for allocating the total longitudinal torque request and/or the total longitudinal speed request, the yaw rate request, and the lateral velocity request to the front drive axle 119F and/or the rear drive axle…”) where the longitudinal torque request (configurations) is split between the axles. Hu provides an example of a simplified three-motor/dual axle system and torque vector that is represented as T = [A,B,C], where each letter in the vector corresponds to a specific torque value to be sent to a different drive unit. The controller calculates these values and the resulting vector defines how the torque is split among the vehicle’s actuators, which is functionally a torque split ratio. In control systems, defining the relationship between inputs and outputs to determine the system’s behavior is the process of mapping. Normalization is an inherent and expected characteristics of such control systems management.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the control system of Hu to incorporate sensing with a sensor a group of configurations to be received by a control unit, coordinating a feedback-based delta slip-speed regulation control and a lateral motion control; coordinating a hybrid boost control; performing non-linear gain scheduling of a controller to regulate dynamics of a wheel slip; and performing a configurations prioritization using the group of configurations, one or more operating points, multiple configuration classifications including: a torque constraint, a torque reference, and an enabling condition to identify a priority to be assigned to individual ones of the elements of the torque; and mapping individual ones of the group of configurations to a normalized torque split ratio. This provides the benefit of improving the vehicle control system to regulate wheel slip and safely coordinating torque request.
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Hu in view of Hubbard et al. (US 20050080539 A1), and herein after will be referred to as Hubbard.
Regarding Claim 15, Hu teaches the method of claim 11 as discussed above. Hu further teaches collecting all of the group of configurations Ri for the set of operating points (o1) in a first collection step (see at least Hu, Para [0007]: “The main controller is configured to receive a set of vehicle inputs indicative of a total longitudinal motion request and a total lateral motion request of the motor vehicle, and to calculate, using the vehicle inputs, a total longitudinal torque request and/or a total longitudinal speed request, a yaw rate request, and a lateral velocity request of the motor vehicle. Hu discloses receiving various control configurations and vehicle inputs which are used as control objectives and constraints.”);
collecting all of the group of configurations Ri for the set of operating points on in a second collection step (see at least Hu, Para [0045]: “Exemplary constraints that could be taken into consideration by the main controller 50 may include, but are not limited to, the tracking of a most efficient torque split between the drive axles 119F and 119R and/or the various road wheels 15F and 15R, constraining wheel slip to a given slip ratio, constraining each assigned axle torque to a corresponding estimated tire capacity, constraining longitudinal velocity for overspeed control, or constraining the total torque to enforce external total torque constraints. Para 61, …. axle torques to be controlled in a closed-loop to track a total driver torque or speed request in different operating modes. The closed-loop control system reevaluates the configuration and constraints in a continuous iterative process.”);
Hu does not explicitly teach inputting an output of the first collection step into a first chart, the first chart including an operating point 1 column and a priority block having priorities ranging from 1 through 5 for an operating point 1; identifying a configuration for individual ones of the priorities for the operating point 1 using the first chart; inputting an output of the second collection step into a second chart, the second chart including an operating point n column and a priority block having priorities ranging from 1 through 5 for the operating point n; and identifying a configuration for individual ones of the priorities for the operating point n using the second chart.
However, Hubbard, in the same field of endeavor teaches inputting an output of the first collection step into a first chart, the first chart including an operating point 1 column and a priority block having priorities ranging from 1 through 5 for an operating point 1 (see at least Hubbard, Para [0044]: “For purposes of the present preferred example, those parameters include input/engine speed and torque (Ni, Ti), output speed and torque (No, To) and modes of operation (Ml, M2)”; see at least Hubbard, [Para 0046]: “In accordance with such a preferred control, No and To are utilized as independent variables in the determination of preferred operating points for the input torque and speed which will allow for torque command generation for the engine and speed control of the transmission via transmission electric motor torque control…FIG. 4 wherein the independent variables are in a first set of columns labeled "inputs" and the dependent variables are in a second set of columns labeled "outputs." Organizing system inputs and outputs into separate columns and rows in structured charts implying the separation and collection of data.”); identifying a configuration for individual ones of the priorities for the operating point 1 using the first chart (see at least Hubbard, Para [0045]: “…visualizing correlation of such operating conditions to such determined data that the operating conditions be generally designated as inputs or independent variables in a matrix or operating space and the determined data be generally designated as outputs or dependent variables. Such input/output arrangement may be better visualized with reference to the top portion 110 of FIG. 4 wherein the determinative independent variables are in a first set of columns labeled "inputs" and the determined dependent variables are in a second set of columns labeled "outputs." The usage of charts to correlate inputs (operating conditions) with optimal outputs aligning with idea of using a chart to identify configurations per priority level.”); inputting an output of the second collection step into a second chart, the second chart including an operating point n column and a priority block having priorities ranging from 1 through 5 for the operating point n (see at least Hubbard, Para [0044]: “For purposes of the present preferred example, those parameters include input/engine speed and torque (Ni, Ti), output speed and torque (No, To) and modes of operation (Ml, M2). Para 46, In accordance with such a preferred control, No and To are utilized as independent variables in the determination of preferred operating points for the input torque and speed which will allow for torque command generation for the engine and speed control of the transmission via transmission electric motor torque control…FIG. 4 wherein the independent variables are in a first set of columns labeled "inputs" and the dependent variables are in a second set of columns labeled "outputs." Organizing system inputs and outputs into separate columns and rows in structured charts implying the separation and collection of data.”); and identifying a configuration for individual ones of the priorities for the operating point n using the second chart (see at least Hubbard, Para [0045]: “…visualizing correlation of such operating conditions to such determined data that the operating conditions be generally designated as inputs or independent variables in a matrix or operating space and the determined data be generally designated as outputs or dependent variables. Such input/output arrangement may be better visualized with reference to the top portion 110 of FIG. 4 wherein the determinative independent variables are in a first set of columns labeled "inputs" and the determined dependent variables are in a second set of columns labeled "outputs." The usage of charts to correlate inputs (operating conditions) with optimal outputs aligning with idea of using a chart to identify configurations per priority level.”).
Therefore, it would have been obvious to one of the ordinary skill of the art before the effective filing date of the claimed invention to modify Hu’s data collection of configurations and operating points to include the teaching of Hubbard’s structured data collection method to organize configurations for operating points. This provides the benefit of improving the efficiency of the prioritization process by correlating operating points to torque configurations.
Response to Arguments
Applicant's arguments filed 05/22/2025 have been fully considered but they are not persuasive.
First, the Applicant has alleged “Hu does not teach each and every element of claim 1 as filed” Specifically, “Hu is absent any teaching of a control unit configured to receive an enabling condition.” The Examiner disagrees. Hu teaches at least two instances where a specific condition enables a change in the controller’s behavior.
The controller modifies its behavior in response to a “mode selection signal” autonomously or manually, such as the driver selecting sports mode. The selection of this mode is the enabling condition that allows the controller to prioritize performance objectives defined by the controller (See at least Hu, Para [0046]: “As part of block B106, the main controller 50 could receive the mode selection signal (arrow MxofFIG. 1) from the mode selection device 22M, whether operator-requested or autonomously-requested. The main controller 50 would then modify weighting within the above-noted cost optimization functions in response to the mode selection signal.).
Furthermore, the controller follows an external request during a “high-slip traction control event” where the sensed event is the enabling condition that triggers a switch in control authority of the vehicle (See at least Hu, Para [0053]: “…performing axle-based arbitration (ARB T AXL) via the main controller 50…such arbitration could include determining, via the main controller 50, whether to follow an optimal torque request generated at block B106, or the request from the external function and limits... Weighting of an external requester function ensures that the main controller 50 selects the request from the external function under appropriate conditions, e.g., during a high-slip traction control event.”).
Second, the Applicant has alleged claims 11, 15, and 18 has been amended in a manner similar to claim 1 and therefore, claims 11, 15, and 18 is in a condition for allowance for at least the same reasons as claim 1. This statement is conclusory and fails to address any specific claim language or examiner’s findings related to claim 11. As required by 37 CFR § 1.111(b), the reply must present arguments pointing out the specific distinctions believed to render the claims, including any newly presented claims, patentable over any applied references… A general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references does not comply with the requirements of this section. Furthermore, as required by 37 CFR § 1.111(c), The applicant or patent owner must also show how the amendments avoid such references or objections. No such explanation has been provided for claims 11, 15, and 18.
Claims 1-11 and 15-18 remain rejected under their respective grounds and rational as cited above. Also, although not specifically argued, all remaining claims remain rejected under their respective grounds, rationales, and applicable prior art for these reasons cited above, and those mentioned in the prior office action which is incorporated herein.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/EDWARD ANDREW IZON DIZON/Examiner, Art Unit 3663
/ANGELA Y ORTIZ/Supervisory Patent Examiner, Art Unit 3663
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